QuantEM — organelle segmentation for electron microscopy
Eight segmentation models covering mitochondria, endoplasmic reticulum, nucleus and lipid droplets, each in two encoder sizes:
| Family | Encoder | Params | Notes |
|---|---|---|---|
| QuantEM | ViT-B/16 | 86 M | Trained from scratch on an EM corpus using the DINOv3 recipe. |
| OmniEM | ViT-L/14 | 302 M | Heads trained on the published OmniEM (EM-DINO) encoder. |
Recommended defaults: QuantEM for mitochondria; OmniEM for ER, nucleus and lipid droplets.
Use
These files are not loaded directly. Install the library, which resolves, downloads, verifies and assembles them for you:
pip install quantem-core
from quantem_em.api import load_model, segment
model = load_model("quantem/mito") # downloads on first use, then cached
labels = segment(model, image, pixel_size_nm=8.0)
For a GUI, install the napari plugin instead — it wraps the same library with segmentation, proofreading, head-only fine-tuning, batch mode and morphometrics:
pip install napari-quantem
Pre-seed a shared or air-gapped cache with:
python -m quantem_em.weights download --all
QUANTEM_MODEL_DIR=/srv/quantem python -m quantem_em.weights verify
Files
Encoders are split so nothing is downloaded twice. The split is not uniform, because the families adapt their encoders differently:
- OmniEM uses LoRA, which never touches the base weights — so
omniem-vitlis a complete encoder genuinely shared by all four heads, and each head is only ~26 MB. - QuantEM mito/nucleus/LD fine-tune the last four blocks, which therefore differ per
organelle.
quantem-vitb-trunkis blocks 0–7 plus embeddings and the final norm; blocks 8–11 ship inside each organelle file. quantem-eris self-contained. It was adapted withfull, replacing the whole encoder, so it needs no trunk.
| File | Size | Contents | SHA-256 |
|---|---|---|---|
quantem-vitb-trunk.safetensors |
227.7 MB | QuantEM ViT-B/16 blocks 0-7 + embeddings + final norm. Shared by the mitochondria, nucleus and lipid-droplet heads, whose own artifacts carry the fine-tuned blocks 8-11. | 637a8c321a7b2172… |
omniem-vitl.safetensors |
1.2 GB | OmniEM (EM-DINO) ViT-L/14 encoder. Untouched by LoRA, so genuinely shared by all four OmniEM heads. | d7f2dffe2ec23138… |
quantem-mito.safetensors |
136.5 MB | model | a897bf322872d1ae… |
quantem-nucleus.safetensors |
136.5 MB | model | d5152b6c2b5ccdbf… |
quantem-ld.safetensors |
136.5 MB | model | 765d1a8e281edf7e… |
quantem-er.safetensors |
465.0 MB | Self-contained: adapt=full replaces the entire encoder, so this needs no trunk. | 50bcdfedc497041f… |
omniem-mito.safetensors |
25.7 MB | model | 7e5c2c8b6ffede26… |
omniem-nucleus.safetensors |
25.7 MB | model | 3142057d3b36b482… |
omniem-ld.safetensors |
25.7 MB | model | ae5b0c356e0fb48d… |
omniem-er.safetensors |
135.2 MB | model | 3e3a693ea757d500… |
Every file is verified against the SHA-256 above on download and on every subsequent load, so a truncated or altered file is detected rather than trusted.
Licence
The weights are released under CC BY 4.0. Commercial use, redistribution and modification are permitted; the only condition is attribution — credit the QuantEM authors, link to the licence, and indicate if you made changes.
Attribution
Full per-source tables, with tile and crop counts and a DOI for every entry, are published as datasets alongside these weights:
ArrojoeDrigoLab/quantem-organelle-model-sources— the annotated ground truth behind these eight models, per organelle.ArrojoeDrigoLab/quantem-base-model-sources— all 655 datasets in the corpus the base encoder was pretrained on.
Citation
Acree et al., QuantEM: An optimized platform of vision transformer-based models for segmentation and analysis of electron microscopy data. Citation details on publication.